Determining a staining-quality parameter of a blood sample

ABSTRACT

Apparatus and methods are described including staining a blood sample with one or more stains. A plurality of microscopic images of the stained blood sample are acquired, using a microscope. Staining-quality parameters for respective microscopic images are determined, using a computer processor, the staining-quality parameters being indicative of a quality of the staining within each of the respective microscopic images. An action is performed by the computer processor, based upon the staining-quality parameters of the respective microscopic images. Other applications are also described.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.16/657,473 to Eshel (published as US 2020/0049970), which is acontinuation of U.S. application Ser. No. 15/760,782 to Eshel (issued asU.S. Pat. No. 10,488,644), which is the US national stage ofInternational Application No. PCT/IL2016/051025 to Eshel (published asWO 17/046799), filed Sep. 15, 2016, which claims priority from U.S.Provisional Patent Application No. 62/219,889 to Eshel, filed Sep. 17,2015, entitled “Methods of detecting a pathogen in a bodily sample andsystem thereof.”

The above-referenced application is incorporated herein by reference.

FIELD OF EMBODIMENTS OF THE INVENTION

Some applications of the presently disclosed subject matter relategenerally to detecting entities in a bodily sample, and in particular,to detecting pathogens automatically using image processing andclassification.

BACKGROUND

The primary method of detection of certain pathogenic infections withina bodily sample (e.g., a blood sample) is the microscopic examination ofthe bodily sample, and visual confirmation of the presence andconcentration of the pathogen. Staining a bodily sample with a stain ordye prior to microscopic examination is often used to enhance contrastin the microscopic image, and to visually highlight cells having aparticular biological makeup. In particular, some fluorescent dyes havean affinity for nucleic acid in cells. When excited by fluorescent lightat an appropriate wavelength, the nucleic acid will fluoresce.Accordingly, fluorescent dyes are sometimes used to differentially stainparts of a cell for detection under a microscope. For example, whenexcited by blue light, the fluorochrome Acridine Orange bound to DNAwill emit green light, and when bound to RNA will emit red light. Bloodpathogens such as Anaplasma marginale, Hemobartonella, trypanosomes,Plasmodium spp., Babesia spp. and others have all been detected withAcridine Orange.

While the primary method of detecting pathogens remains visualidentification in a microscopic bright field image, fluorescentmicroscopy has been used as well, though to a lesser extent. However, inboth cases, detection of a pathogenic infection by manual identificationof pathogens suffers from two main drawbacks: many settings (especiallyrural) are not equipped to perform the test, and the accuracy of theresults depends on both the skill of the person examining the sample andthe levels of the pathogen in the sample. Accordingly, attempts havebeen made to automate the detection of pathogens in a bodily sample.

SUMMARY OF EMBODIMENTS

In accordance with some applications of the present invention, one ormore microscope images of a bodily sample (e.g., a blood sample) areacquired, using a microscope of a microscope system. A computerprocessor identifies at least one element as being a pathogen candidate(i.e., a constituent element within the sample that exhibitscharacteristics that indicate that it may be a pathogen, and istherefore a candidate for being a pathogen) within the images. Forexample, the images may be images of a blood sample that were acquiredwhile the sample was stained with a stain or dye that is configured tostain DNA and/or RNA within the sample, and the computer processor mayidentify the candidate by detecting stained elements (e.g., fluorescingelements) within the images. The computer processor extracts, from theone or more images, at least one candidate-informative featureassociated with the pathogen candidate, and at least onesample-informative feature that is indicative of contextual informationrelated to the bodily sample. The likelihood of the bodily sample beinginfected with a pathogenic infection is classified by processing thecandidate-informative feature in combination with the sample-informativefeature. An output is typically generated on an output device inresponse to the classification.

For some applications, in response to the candidate-informative feature,the computer processor performs a first classifying, in which alikelihood of the pathogen candidate being a pathogen is classified. Inresponse to the first classifying in combination with thesample-informative feature, the computer processor a second classifyingin which a likelihood of the bodily sample containing a pathogenicinfection is classified. For some applications, the first classifying(in which a likelihood of the pathogen candidate being a pathogen isclassified) is performed in response to the candidate-informativefeature in combination with the sample-informative feature. For someapplications, the computer processor classifies a pathogenic infectionin the bodily sample as a given type of pathogenic infection (e.g.,Plasmodium, a given strain of Plasmodium, and/or Plasmodium of a givenage or age range), by processing the candidate-informative feature incombination with the sample-informative feature.

For some applications, the candidate-informative feature includes a sizeof the pathogen candidate (e.g. dimension, length, circumference,minimum width, maximum width, area, and/or relative size of thecandidate with respect to other candidates or entities), a shape of thepathogen candidate, a motion of the pathogen candidate, an intensity ofthe pathogen candidate, a location of the pathogen candidate within thebodily sample (including proximity, abutment, and/or overlap of thecandidate with respect to other candidates or entities), a property of acell overlapping the pathogen candidate, a color of the pathogencandidate (including intensity and pattern of staining), a texture(e.g., contour) of the pathogen candidate, and/or a sharpness of aboundary of the pathogen candidate. Further non-limiting examples ofcandidate-informative features are described for example in US2012/0169863 to Bachelet, and/or US 2015/0037806 to Pollak, both ofwhich applications are incorporated herein by reference.

For some applications, sample-informative features include a size of oneor more non-pathogen-candidate constituents in the bodily sample, ashape of one or more non-pathogen-candidate constituents within thebodily sample, an intensity of one or more non-pathogen-candidateconstituents within the bodily sample, a quantity of cells of a givencell type within the bodily sample, a distribution of cells of a givencell type within the bodily sample, and/or a distribution of pathogencandidates within the bodily sample.

There is therefore provided, in accordance with some applications of thepresent invention, apparatus including:

a microscope system configured to acquire one or more microscope imagesof a bodily sample;

an output device; and

at least one computer processor configured to:

-   -   identify, in the one or more images, at least one element as        being a pathogen candidate,    -   extract, from the one or more images, at least one        candidate-informative feature associated with the pathogen        candidate,    -   extract, from the one or more images, at least one        sample-informative feature that is indicative of contextual        information related to the bodily sample;    -   classifying a likelihood of the bodily sample being infected        with a pathogenic infection, by processing the        candidate-informative feature in combination with the        sample-informative feature, and    -   generate an output upon the output device, in response thereto.

In some applications:

the microscope system is configured to acquire one or more microscopeimages of a bodily sample that is stained with a stain; and

the at least one computer processor is configured to identify at leastone element as being a pathogen candidate by identifying the at leastone element as being a pathogen candidate by identifying that the atleast one element is stained.

In some applications, the at least one computer processor is configuredto process the candidate-informative feature in combination with thesample-informative feature by:

in response to the candidate-informative feature, performing a firstclassifying, in which a likelihood of the pathogen candidate being apathogen is classified, and

in response to the first classifying in combination with thesample-informative feature, performing a second classifying in which alikelihood of the bodily sample containing a pathogenic infection isclassified.

In some applications, the at least one computer processor is configuredto process the candidate-informative feature in combination with thesample-informative feature by:

in response to the candidate-informative feature in combination with thesample-informative feature, performing a first classifying, in which alikelihood of the pathogen candidate being a pathogen is classified, and

at least partially in response to the first classifying, performing asecond classifying in which in which a likelihood of the bodily samplecontaining a pathogenic infection is classified.

In some applications, the at least one computer processor is configuredto extract, from the one or more images, at least onecandidate-informative feature associated with the pathogen candidate byextracting, from the one or more images, at least onecandidate-informative feature associated with the pathogen candidate,the candidate-informative feature being a feature selected from thegroup consisting of: a size of the pathogen candidate, a shape of thepathogen candidate, a motion of the pathogen candidate, an intensity ofthe pathogen candidate, a location of the pathogen candidate within thebodily sample, a property of a cell overlapping the pathogen candidate,a color of the pathogen candidate, a texture of the pathogen candidate,and a sharpness of a boundary of the pathogen candidate.

In some applications, the at least one computer processor is configuredto extract, from the one or more images, at least one sample-informativefeature that is indicative of contextual information related to thebodily sample by extracting, from the one or more images, at least onesample-informative feature selected from the group consisting of: a sizeof one or more non-pathogen-candidate constituents in the bodily sample,a shape of one or more non-pathogen-candidate constituents within thebodily sample, an intensity of one or more non-pathogen-candidateconstituents within the bodily sample, a quantity of cells of a givencell type within the bodily sample, a distribution of cells of a givencell type within the bodily sample, and a distribution of pathogencandidates within the bodily sample.

In some applications:

the microscope system is configured to acquire the one or moremicroscope images of the bodily sample by acquiring one or moremicroscope images of a bodily sample that is stained with a stain; and

the at least one computer processor is configured to extract, from theone or more images, at least one sample-informative feature that isindicative of contextual information related to the bodily sample byextracting, from the one or more images, at least one sample-informativefeature that is indicative of a quality of staining of the bodily sampleby the stain.

In some applications, the at least one computer processor is configuredto extract, from the one or more images, at least one sample-informativefeature that is indicative of contextual information related to thebodily sample by extracting, from the one or more images, at least onesample-informative feature that is indicative of a foreign object thatis present in the bodily sample.

In some applications, the bodily sample includes a bodily sampleselected from the group consisting of: a blood sample, a diluted bloodsample, a sample including predominantly red blood cells, and a dilutedsample including predominantly red blood cells, and the microscopesystem is configured to acquire one or more images of the selectedbodily sample.

In some applications, the at least one computer processor is configuredto extract, from the one or more images, at least one sample-informativefeature that is indicative of contextual information related to thebodily sample by extracting, from the one or more images, a size of oneor more red blood cells that are present within the bodily sample.

In some applications, the at least one computer processor is configuredto extract, from the one or more images, at least one sample-informativefeature that is indicative of contextual information related to thebodily sample by extracting, from the one or more images, an indicationof a presence of Howell Jolly bodies within the bodily sample.

In some applications, the at least one computer processor is configuredto extract, from the one or more images, at least one sample-informativefeature that is indicative of contextual information related to thebodily sample by extracting, from the one or more images, aconcentration of platelets within the bodily sample.

In some applications, the at least one computer processor is configuredto extract, from the one or more images, at least one sample-informativefeature that is indicative of contextual information related to thebodily sample by extracting, from the one or more images, a relationshipbetween a number of reticulocytes associated with candidates and anumber of mature red blood cells associated with candidates.

In some applications, the at least one computer processor is configuredto extract, from the one or more images, at least one sample-informativefeature that is indicative of contextual information related to thebodily sample by extracting, from the one or more images, aconcentration of reticulocyte bodies within the bodily sample.

In some applications, the at least one computer processor is configuredto classify the likelihood of the bodily sample being infected with thepathogenic infection by adjusting a threshold for a positivedetermination of a pathogenic infection, based upon the concentration ofthe reticulocyte bodies within the bodily sample.

In some applications, the at least one computer processor is configuredto classify a pathogenic infection in the bodily sample as containingone or more given types of pathogen, by processing thecandidate-informative feature in combination with the sample-informativefeature.

In some applications, the at least one computer processor is configuredto classify the pathogenic infection in the bodily sample as containingone or more given types of pathogen by classifying the pathogenicinfection as containing one or more categories of pathogen selected fromthe group consisting of: Plasmodium, a given strain of Plasmodium,Plasmodium of a given age, and Plasmodium of a given age range.

In some applications:

the bodily sample includes a bodily sample selected from the groupconsisting of: a blood sample, a diluted blood sample, a samplecomprising predominantly red blood cells, and a diluted samplecomprising predominantly red blood cells;

the at least one computer processor is configured to extract, from theone or more images, at least one sample-informative feature that isindicative of contextual information related to the bodily sample byextracting, from the one or more images, a relationship between a numberof reticulocytes associated with candidates and a number of mature redblood cells associated with candidates; and

the at least one computer processor is configured to classify thepathogenic infection in the bodily sample as containing one or moregiven types of pathogen by classifying the pathogenic infection in thebodily sample as containing the given type of pathogen, at leastpartially based upon the relationship between a number of reticulocytesassociated with candidates and a number of mature red blood cellsassociated with candidates.

In some applications:

the bodily sample includes a bodily sample selected from the groupconsisting of: a blood sample, a diluted blood sample, a samplecomprising predominantly red blood cells, and a diluted samplecomprising predominantly red blood cells;

the at least one computer processor is configured to extract, from theone or more images, at least one sample-informative feature that isindicative of contextual information related to the bodily sample byextracting, from the one or more images, shapes of red blood cellswithin the bodily sample, and

the at least one computer processor is configured to classify thepathogenic infection in the bodily sample as containing the given typeof pathogen by classifying the pathogenic infection in the bodily sampleas the given type of pathogenic infection, at least partially based uponthe shapes of the red blood cells within the bodily sample.

In some applications:

the bodily sample includes a bodily sample selected from the groupconsisting of: a blood sample, a diluted blood sample, a samplecomprising predominantly red blood cells, and a diluted samplecomprising predominantly red blood cells;

the at least one computer processor is configured to extract, from theone or more images, at least one sample-informative feature that isindicative of contextual information related to the bodily sample byextracting, from the one or more images, sizes of red blood cells withinthe bodily sample, and

the at least one computer processor is configured to classify thepathogenic infection in the bodily sample as containing the given typeof pathogen by classifying the pathogenic infection in the bodily sampleas the given type of pathogenic infection, at least partially based uponthe sizes of the red blood cells within the bodily sample.

There is further provided, in accordance with some applications of thepresent invention, a method including:

acquiring one or more microscope images of a bodily sample, using amicroscope;

using at least one computer processor:

-   -   in the one or more images, identifying at least one element as        being a pathogen candidate;    -   extracting, from the one or more images, at least one        candidate-informative feature associated with the pathogen        candidate;    -   extracting, from the one or more images, at least one        sample-informative feature that is indicative of contextual        information related to the bodily sample;    -   classifying a likelihood of the bodily sample being infected        with a pathogenic infection, by processing the        candidate-informative feature in combination with the        sample-informative feature; and    -   generating an output, in response thereto.

There is further provided, in accordance with some applications of thepresent invention, a computer software product, for use with a bodilysample, an output device, and a microscope system configured to acquireone or more microscope images of a bodily sample, the computer softwareproduct including a non-transitory computer-readable medium in whichprogram instructions are stored, which instructions, when read by acomputer cause the computer to perform the steps of: in the one or moreimages, identifying at least one element as being a pathogen candidate;extracting, from the one or more images, at least onecandidate-informative feature associated with the pathogen candidate;extracting, from the one or more images, at least one sample-informativefeature that is indicative of contextual information related to thebodily sample; classifying a likelihood of the bodily sample beinginfected with a pathogenic infection, by processing thecandidate-informative feature in combination with the sample-informativefeature; and generating an output upon the output device, in responsethereto.

There is further provided, in accordance with some applications of thepresent invention, apparatus including:

a microscope system configured to acquire one or more microscope imagesof a bodily sample;

an output device; and

at least one computer processor configured to:

-   -   extract, from the one or more images, at least one        sample-informative feature that is indicative of contextual        information related to the bodily sample,    -   at least partially based upon the extracted sample-informative        feature:        -   identify that there is a defect associated with the bodily            sample disposed in the sample carrier, and        -   classify a source of the defect, and    -   in response thereto, generate an output on the output device        that is indicative of the source of the defect.

In some applications, the at least one computer processor is configuredto classify the source of the defect by classifying the source as beingat least one source selected from the group consisting of: the samplecarrier, a given portion of the sample carrier, the bodily sample, and adiluent in which the sample has been diluted.

There is further provided, in accordance with some applications of thepresent invention, a method including:

acquiring one or more microscope images of a bodily sample disposed in asample carrier, using a microscope;

using at least one computer processor:

-   -   extracting, from the one or more images, at least one        sample-informative feature that is indicative of contextual        information related to the bodily sample;    -   at least partially based upon the extracted sample-informative        feature:        -   identifying that there is a defect associated with the            bodily sample disposed in the sample carrier, and        -   classifying a source of the defect; and    -   in response thereto, generating an output that is indicative of        the source of the defect.

There is further provided, in accordance with some applications of thepresent invention, a computer software product, for use with a bodilysample, an output device and a microscope system configured to acquireone or more microscope images of a bodily sample, the computer softwareproduct including a non-transitory computer-readable medium in whichprogram instructions are stored, which instructions, when read by acomputer cause the computer to perform the steps of: extracting, fromthe one or more images, at least one sample-informative feature that isindicative of contextual information related to the bodily sample; atleast partially based upon the extracted sample-informative feature:identifying that there is a defect associated with the bodily sampledisposed in the sample carrier, and classifying a source of the defect;and in response thereto, generating an output on the output device thatis indicative of the source of the defect.

There is further provided, in accordance with some applications of thepresent invention, apparatus for classifying a bodily sample, theapparatus including:

a microscope system configured to acquire one or more microscope imagesof the bodily sample;

an output device; and

at least one computer processor configured to:

-   -   identify, in the one or more images, at least one element as        being a candidate of a given entity,    -   extract, from the one or more images, at least one        candidate-informative feature associated with the identified        element,    -   extract, from the one or more images, at least one        sample-informative feature that is indicative of contextual        information related to the bodily sample,    -   process the candidate-informative feature in combination with        the sample-informative feature, and    -   generate an output upon the output device, in response thereto.

In some applications, the bodily sample includes a sample that containsblood, and the computer processor is configured to identify at least oneelement as being a candidate of a given entity by identifying at leastone element as being a candidate of a given entity within the blood.

In some applications, the computer processor is configured to identifyat least one element as being a candidate of a given entity byidentifying at least one element as being a pathogen candidate.

There is further provided, in accordance with some applications of thepresent invention, a method for classifying a bodily sample, the methodincluding:

acquiring one or more microscope images of the bodily sample, using amicroscope;

using at least one computer processor:

-   -   in the one or more images, identifying at least one element as        being a candidate of a given entity;    -   extracting, from the one or more images, at least one        candidate-informative feature associated with the identified        element;    -   extracting, from the one or more images, at least one        sample-informative feature that is indicative of contextual        information related to the bodily sample;    -   processing the candidate-informative feature in combination with        the sample-informative feature; and    -   generating an output, in response thereto.

In some applications, the bodily sample includes a sample that containsblood, and identifying at least one element as being a candidate of agiven entity includes identifying at least one element as being acandidate of a given entity within the blood.

In some applications, identifying at least one element as being acandidate of a given entity includes identifying at least one element asbeing a pathogen candidate.

There is further provided, in accordance with some applications of thepresent invention, a computer software product, for use with a bodilysample, an output device and a microscope system configured to acquireone or more microscope images of a bodily sample, the computer softwareproduct including a non-transitory computer-readable medium in whichprogram instructions are stored, which instructions, when read by acomputer cause the computer to perform the steps of: in the one or moreimages, identifying at least one element as being a candidate of a givenentity; extracting, from the one or more images, at least onecandidate-informative feature associated with the identified element;extracting, from the one or more images, at least one sample-informativefeature that is indicative of contextual information related to thebodily sample; processing the candidate-informative feature incombination with the sample-informative feature; and generating anoutput on the output device, in response thereto.

There is further provided, in accordance with some applications of thepresent invention, apparatus including:

a microscope system configured to acquire one or more microscope imagesof a bodily sample;

an output device; and

at least one computer processor configured to:

-   -   in the one or more images, identify at least one element as        being a candidate of a given entity,    -   extract, from the one or more images, at least one        candidate-informative feature associated with the candidate,    -   extract, from the one or more images, at least one        sample-informative feature that is indicative of contextual        information related to the bodily sample,    -   process the candidate-informative feature in combination with        the sample-informative feature, and    -   in response thereto, perform an action selected from the group        consisting of: generating an output on the output device        indicating that presence of an infection within the bodily        sample could not be determined with a sufficient degree of        reliability, generating an output on the output device        indicating that a portion of the sample should be re-imaged,        generating an output on the output device indicating that a        portion of the sample should be re-imaged using different        settings, driving the microscope system to re-image a portion of        the sample, driving the microscope system to re-image a portion        of the sample using different settings, and modulating a frame        rate at which microscope images are acquired by the microscope        system.

There is further provided, in accordance with some applications of thepresent invention, a method including:

acquiring one or more microscope images of a bodily sample, using amicroscope;

using at least one computer processor:

-   -   in the one or more images, identifying at least one element as        being a candidate of a given entity;    -   extracting, from the one or more images, at least one        candidate-informative feature associated with the candidate;    -   extracting, from the one or more images, at least one        sample-informative feature that is indicative of contextual        information related to the bodily sample;    -   processing the candidate-informative feature in combination with        the sample-informative feature; and    -   in response thereto, performing an action selected from the        group consisting of: generating an output indicating that        presence of an infection within the bodily sample could not be        determined with a sufficient degree of reliability, generating        an output indicating that a portion of the sample should be        re-imaged, generating an output indicating that a portion of the        sample should be re-imaged using different settings, driving the        microscope to re-image a portion of the sample, driving the        microscope to re-image a portion of the sample using different        settings, and modulating a frame rate at which microscope images        are acquired by the microscope.

There is further provided, in accordance with some applications of thepresent invention, a computer software product, for use with a bodilysample, an output device and a microscope system configured to acquireone or more microscope images of a bodily sample, the computer softwareproduct including a non-transitory computer-readable medium in whichprogram instructions are stored, which instructions, when read by acomputer cause the computer to perform the steps of: in the one or moreimages, identifying at least one element as being a candidate of a givenentity; extracting, from the one or more images, at least onecandidate-informative feature associated with the candidate; extracting,from the one or more images, at least one sample-informative featurethat is indicative of contextual information related to the bodilysample; processing the candidate-informative feature in combination withthe sample-informative feature; and in response thereto, performing anaction selected from the group consisting of: generating an output onthe output device indicating that presence of an infection within thebodily sample could not be determined with a sufficient degree ofreliability, generating an output on the output device indicating that aportion of the sample should be re-imaged, generating an output on theoutput device indicating that a portion of the sample should bere-imaged using different settings, driving the microscope system tore-image a portion of the sample, driving the microscope system tore-image a portion of the sample using different settings, andmodulating a frame rate at which microscope images are acquired by themicroscope system.

There is further provided, in accordance with some applications of thepresent invention, apparatus including:

a microscope system configured to acquire one or more microscope imagesof a bodily sample;

an output device; and

at least one computer processor configured to:

-   -   identify within one or more images of the set of images elements        as being candidates of one or more given entities,    -   extract, from the one or more images, candidate-informative        features associated with the candidates,    -   extract, from the candidate-informative features, two or more        sample-informative features related to the bodily sample,    -   determine a characteristic of the bodily sample, by processing        the two or more sample-informative features, and    -   generate an output, in response thereto.

In some applications, the bodily sample includes a bodily sample thatcontains blood, and the computer processor is configured to extract thecandidate-informative features associated with the candidates byextracting one or more candidate-informative features associated with apathogen candidate within the blood, and extracting one or morecandidate informative features associated with platelets within theblood.

In some applications, the bodily sample includes a bodily sample thatcontains blood, and the computer processor is configured to extract thecandidate-informative features associated with the candidates byextracting one or more candidate-informative features associated with apathogen candidate within the blood, and extracting one or morecandidate informative features associated with reticulocytes within theblood.

In some applications:

the bodily sample includes a bodily sample that contains blood,

the computer processor is configured to identify within one or moreimages of the set of images elements as being candidates of one or moregiven entities by identifying elements as being pathogen candidates, and

the computer processor is configured to extract, from thecandidate-informative features, two or more sample-informative featuresrelated to the bodily sample by extracting, from thecandidate-informative features, two or more sample-informative featuresselected from the group consisting of: number of pathogen candidates inthe sample, type of pathogen candidates in the sample, brightness of thecandidates relative to background brightness, a probability ofcandidates being pathogens, number of candidates that have a probabilityof being a pathogen that exceeds a threshold, number of candidates thathave a probability of being a given type of pathogen that exceeds athreshold, a number of platelets in the sample, brightness of platelets,a number of reticulocytes in the sample, a number of reticulocytesinfected by pathogens in the sample, a proximity of the candidates tored blood cells, and a number of red blood cells in the sample.

There is further provided, in accordance with some applications of thepresent invention, a method for classifying a bodily sample, the methodincluding:

acquiring a set of microscope images of the bodily sample, using amicroscope;

using at least one computer processor:

-   -   identifying within one or more images of the set of images        elements as being candidates of one or more given entities;    -   extracting, from the one or more images, candidate-informative        features associated with the candidates,    -   extracting, from the candidate-informative features, two or more        sample-informative features related to the bodily sample;    -   determining a characteristic of the bodily sample, by processing        the two or more sample-informative features; and    -   generating an output, in response thereto.

There is further provided, in accordance with some applications of thepresent invention, a computer software product, for use with a bodilysample, an output device and a microscope system configured to acquireone or more microscope images of a bodily sample, the computer softwareproduct including a non-transitory computer-readable medium in whichprogram instructions are stored, which instructions, when read by acomputer cause the computer to perform the steps of: identifying withinone or more images of the set of images elements as being candidates ofone or more given entities; extracting, from the one or more images,candidate-informative features associated with the candidates;extracting, from the candidate-informative features, two or moresample-informative features related to the bodily sample; determining acharacteristic of the bodily sample, by processing the two or moresample-informative features; and generating an output on the outputdevice, in response thereto.

The present invention will be more fully understood from the followingdetailed description of embodiments thereof, taken together with thedrawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a generalized functional diagram of a pathogen detectionsystem, in accordance some applications of the present invention;

FIG. 2 is a generalized flow chart of steps that are performed, inaccordance with some applications of the present invention;

FIG. 3 is a non-limiting example of imaging information that isanalyzed, in accordance with some applications of the present invention;and

FIG. 4 is a non-limiting illustration of a relative location of anRNA-stained region and a DNA-stained region, in accordance with someapplications of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS

Reference is now made to FIG. 1, which is a functional diagram of apathogen detection system 10, in accordance with some applications ofthe present invention. Pathogen detection system 10 includes a processor28 operatively coupled to a memory 30, e.g. by a communication bus 31.In certain embodiments, pathogen detection system 100 can optionallyinclude or be operatively coupled to a microscope system 11. Microscopesystem 11 is typically a digital microscope that includes an imagingmodule 14, a focus variation module 16, a sample carrier 18 and anautofocus system 20. For some applications, microscope system 11 isgenerally similar to the microscope system described in US 2014/0347459to Greenfield, which is incorporated herein by reference.

Typically, imaging module 14 includes an optical unit 22 and an imagesensor unit 24. Optical unit 22 is configured to form a magnified imageof a bodily sample 12 (for example, a blood sample) by conjugating afocus plane 36 and an image plane. The image sensor unit 24 typicallyincludes an image sensor, for example a charge-coupled-device (CCD),complementary metal-oxide-semiconductor (CMOS) sensor, and/or a matrixsensor, positioned in the image plane of the optical unit 22 so as tosense the magnified image.

Computer processor 28 typically receives and processes images. Thecomputer processor communicates with memory 30, and images are receivedby the processor via the memory. Via a user interface 32, a user (e.g.,a laboratory technician) sends instructions to the computer processor.For some applications, the user interface includes a keyboard, a mouse,a joystick, a touchscreen device (such as a smartphone or a tabletcomputer), a touchpad, a trackball, a voice-command interface, and/orother types of user interfaces that are known in the art. Typically, thecomputer processor generates an output via an output device 34. Furthertypically, the output device includes a display, such as a monitor, andthe output includes an output that is displayed on the display. For someapplications, the processor generates an output on a different type ofvisual, text, graphics, tactile, audio, and/or video output device,e.g., speakers, headphones, a smartphone, or a tablet computer. For someapplications, user interface 32 acts as both an input interface and anoutput interface, i.e., it acts as an input/output interface. For someapplications, the processor generates an output on a computer-readablemedium (e.g., a non-transitory computer-readable medium), such as adisk, or a portable USB drive, and/or generates an output on a printer.

Microscope system 11 can, in certain embodiments, include a localprocessor that controls at least some of the processes of microscopesystem 11, for example, image acquisition and/or communication withother components, including other components of pathogen detectionsystem 10 and components external to pathogen detection system 10. Incertain other embodiments, processor 28 can control one or moreprocesses of microscope system 11 including, e.g. image acquisitionand/or communication. Optionally, pathogen detection system 10 caninclude or be operatively coupled to a plurality of digital microscopes.Optionally, each respective digital microscope in the plurality ofdigital microscopes has its own local processor.

In certain embodiments, memory 30 can be configured to store imaginginformation, program data and/or executable program instructions fordetecting a pathogen in a bodily sample, as will be detailed below withreference to FIG. 2. Memory 30 can be, e.g., volatile memory ornon-volatile memory. In certain embodiments, memory 30 is non-volatilememory, e.g. hard disk drive, flash memory, etc.

For some applications, microscope system 11 is configured to capture oneor more high magnification digital images of a bodily sample.Optionally, the one or more digital images include images that coverdifferent portions of the bodily sample. Optionally, the images do notoverlap (or overlap by less than 5 percent or less than 1 percent).Optionally, the images include images that overlap and are taken atdifferent depths of focus, and/or with different lighting conditions.Optionally, the one or more digital images include sets of images thatdo not overlap (or overlap by less than 5 percent or less than 1percent), but each of the sets includes images of another set, takenwith different lighting conditions. In certain embodiments, microscopesystem 11 is configured to capture images under a plurality of lightingconditions, including, e.g., bright field, blue light, and ultravioletlight, as will be further detailed below.

In accordance with some applications, bodily sample 12 (e.g., a bloodsample) is scanned by the microscope system, such that a plurality ofportions of the bodily sample are imaged. For some applications, aplurality of images are acquired of one or more portions of the bodilysample, with each of the plurality of images being acquired underrespective imaging conditions. For example, two images of a portion ofthe bodily sample may be acquired using, respectively, imagingconditions that allow detection of cells (e.g., bright-field) andimaging conditions that allow visualization of stained bodies (e.g.appropriate fluorescent illumination).

Image sensor unit 24 may output acquired digital images to output device34 (which may include a display) and/or to the autofocus system 20.Focus variation module 16 may be configured to vary a distance betweenthe focus plane 36 of the optical unit 22 and the sample carrier 18.Focus variation module 16 may be operated manually or automatically viaa mechanical interface which may, for example, modify the position ofthe sample carrier 18 along an optical axis Z of the optical unit 22.Alternatively or additionally, focus variation module 16 may becommanded by autofocus system 20. For example, the focus variationmodule 16 may vary the distance between the sample carrier 18 and thefocus plane by (1) modifying the position of optical unit 22 along theoptical axis Z, (2) modifying the position of the sample carrier 18along the position of the optical axis Z (e.g., by moving a stage uponwhich the sample carrier is placed), (3) modifying the position of thefocus plane by, for example, changing a focal length of the optical unit22, or a combination thereof.

The sample carrier 18 may comprise a plate. Sample carrier 18 may beconfigured to accommodate bodily sample 12. The carrier may be anycarrier known in the art for holding a biological sample. Optionally,the bottom surface of the carrier is essentially flat, to allow cells incontact therewith to be at about the same distance from the focal planeof the microscope. Examples include carrier slides, laboratoryreceptacles, dishes, plates, multi-well plates, test tubes (e.g. with aflat bottom), microfluidic cells, cartridges, and the like.

Autofocus system 20 may comprise an autofocus computation module 38 andan autofocus adaption module 39. The autofocus computation module may beconnected to the image sensor unit 24 so as to receive images acquiredby the imaging module 14. The autofocus adaptation module may beconnected to the focus variation module 16 and may be configured tocommand the focus variation module 16, e.g., as described above.

For some applications, processor 28 includes one or more functionalmodules, such as a feature extraction module, a candidate classifier, asample classifier, and a diagnostics module. For some applications,processor 28 is configured to process imaging information by extractingfeatures contained within the imaging information. Typically, theprocessor is configured to extract at least one sample-informativefeature and at least one candidate-informative feature. For someapplications, the processor is further configured to process the atleast one sample-informative feature to obtain contextual information,and to process the at least one candidate-informative feature to obtaincandidate data, as will be further detailed below.

Typically, the processor is configured to classify a likelihood of acandidate (i.e., a constituent element within the sample that exhibitscharacteristics that indicate that it may be a pathogen, and istherefore a candidate for being a pathogen) being a pathogen at leastpartially based upon the at least one candidate-informative feature.Further typically, the processor is configured to classify a likelihoodof the bodily sample being infected with a pathogenic infection, byprocessing the at least one candidate-informative feature in combinationwith the at least one sample-informative feature.

For some applications, the processor is programmed to classify thelikelihood of a candidate being a pathogen, and/or to classify alikelihood of sample being infected with a pathogenic infection usingclassification and/or machine learning algorithms, e.g. support vectormachines, neural networks, naive Bayes algorithms, etc. Additionalexamples of types of classification and/or machine learning algorithmswhich can be used by the processor are described in US 2012/0169863 toBachelet and/or US 2015/0037806 to Pollak, both of which applicationsare incorporated herein by reference. For some applications, thecomputer processor is trained, in advance of being used to analyze abodily sample, using training images of bodily samples.

For some applications, if a bodily sample is determined to be infectedwith a pathogenic infection (or if it is determined that the likelihoodof the bodily sample being infected with a pathogenic infection exceedsa threshold), the computer processor is further configured to extractdiagnostic information about the pathogenic infection in accordance withat least the at least one sample-informative feature.

It is noted that the teachings of the presently disclosed subject matterare not bound by the specific pathogen detection system described withreference to FIG. 1. Equivalent and/or modified functionality can beconsolidated or divided in another manner and can be implemented in anyappropriate combination of software, firmware and hardware. Theprocessor can be implemented as a suitably programmed computer.

Reference is now made to FIG. 2, which shows a generalized flow chart ofa method for detecting a pathogenic infection in a bodily sample (e.g.,a blood sample), in accordance with some applications of the presentinvention.

In a first step 200, one or more images of the bodily sample areacquired by microscope system 11. The one or more images, datainformative of one or more images, or data derived from one or moreimages (collectively referred to herein as “imaging information”) aretypically stored in memory 30. The imaging information is then analyzedby processor 28, as described in further detail hereinbelow. It is notedthat in the present application, the computer processor is described asextracting features from the one or more images. This terminology shouldbe interpreted as including extracting the features from datainformative of the one or more images, or data derived from the one ormore images, and should not be interpreted as being limited to directlyextracting the features from the one or more images themselves.

For some applications, the imaging information is informative of atleast one high magnification microscopic view of the sample.Alternatively or additionally, the imaging information is informative ofa plurality of images, including, e.g., images of different portions ofthe sample, images of the same portion of the sample taken at differentfocal depths, and/or different lighting conditions, and/or at differenttimes.

The bodily sample may be from any living creature but preferably fromwarm blooded animals. Typically, the bodily sample is a blood sample.The sample can be any blood sample or a portion thereof comprising oneor more red blood cells. Optionally, the sample comprises predominantlyred blood cells (i.e., a majority of the cells (e.g., at least 60percent of the cells) in the sample are red blood cells). Optionally,the sample also comprises at least one of platelets and white bloodcells. Optionally, the blood sample is diluted. Optionally, the dilutionis performed or the sample is otherwise prepared such that theconcentration of cells on the surface that is imaged is between 3,000and 30,000 cells (e.g., red blood cells) per square mm. Optionally, theblood sample is diluted with a staining solution.

Optionally, the sample or staining solution comprises one or moresuitable dyes or stains (optionally, comprising one or more fluorescentdyes). In some embodiments, the blood sample is selected from wholeblood sample, red blood cell sample, buffy coat sample, plasma sample,serum sample, a sample from any other blood fraction, or any combinationthereof.

Optionally, the sample forms a monolayer on the surface of samplecarrier 18. In the context of the present disclosure, when referring toa monolayer of cells, it is to be understood as encompassing thedistribution of cells on a surface as an essentially single layer, whereat least 50 percent (at times, at least 60 percent, 70 percent, 80percent or even 90 percent) of the cells are in direct contact with thebottom surface of the carrier and not more than 20 percent (at times, nomore than 10 percent or even no more than 5 percent) of the cellsoverlay each other (i.e., no more than the aforementioned percentage ofcells lie, partially or completely, on top of one another). Further,when referring to a monolayer, it is to be understood that at least 5percent (at times, at least 10 percent or even at least 20 percent) ofthe cells touch each other on the bottom surface. For some applications,a monolayer is formed in accordance with the techniques described inU.S. Pat. No. 9,329,129 to Pollak, which is incorporated herein byreference.

For some applications, prior to being imaged, the bodily sample isstained with one or more suitable dyes or stains. Optionally, the one ormore suitable dyes or stains comprise one or more fluorescent dyes orstains, and the stained sample is excited under one or more suitablelighting conditions for detecting pathogens. As used herein, the term“suitable dye or stain” should be expansively construed to include anydye or stain useful for the detection of a pathogen of interest,including any suitable fluorescent dye or stain. As used herein, a“suitable fluorescent dye or stain” should be expansively construed toinclude a dye or stain which is capable of selectively binding to one ormore types of nucleic acid (e.g., DNA only, RNA only, both DNA and RNA,etc.) and fluoresces under one or more particular lighting conditionsthereby allowing for discerning of the one or more types of nucleicacids in a bodily sample. Suitable fluorescent dyes or stains caninclude, for example, dyes or stains that bind to DNA and do not bind toRNA, dyes or stains that bind to RNA and do not bind to DNA, and dyes orstains that bind to both DNA and RNA. Non-limiting examples of suitablefluorescent dyes or stains include, e.g., Acridine Orange, Hoechststain, etc.

The particular lighting condition which causes a particular suitablefluorescent dye or stain to fluoresce is referred to herein as a“suitable lighting condition,” which should be expansively construed toinclude a lighting condition which, when used to excite a particularfluorescent dye or stain, causes fluorescing of the fluorescent dye orstain. In certain embodiments, the fluorescence emitted by the exciteddye or stain may be discernible through the use of one or more differentlight filters which enable the discerning of fluorescence within a givenwavelength range. Accordingly, suitable lighting conditions may be usedin view of such filters. Non-limiting examples of suitable lightingconditions include, e.g., bright field, blue light, and ultravioletlight. Additional non-limiting examples of suitable fluorescent dyes orstains and suitable lighting conditions are described in US 2012/0169863to Bachelet and US 2015/0037806 to Pollak, both of which applicationsare incorporated herein by reference.

As detailed above, in certain embodiments, the sample may be stainedwith one or more dyes or stains that allow differentiating between RNAand DNA in the sample (i.e., differential staining). Differentialstaining can be accomplished, for example, by staining the sample withone or more target-specific dyes or stains. As used herein atarget-specific dye or stain (e.g., an RNA-specific or a DNA-specific)is a dye or stain that under selected conditions would detectably stainthe target moiety such that it may be detected in the presence of othercellular components. In this context, detectably staining a target maymean that the dye or stain binds to the target with a higher affinitythan to other cellular components and/or that it provides a strongersignal (e.g. fluorescence) when associated with the target. It is notedthat some dyes or stains may stain more than one target but may bedifferentiated for example based on the wavelength of emittedfluorescence and/or a wavelength used for excitation of the dye orstain. In some embodiments, a target-specific dye or stain is afluorescent dye or stain that upon binding to the target shifts itsemission wavelength from an original band to a shifted band. In suchcases, the target may be detected by a system configured to detectemission wavelengths within the shifted band.

Differential staining may be used to determine the relative locations ofDNA and RNA, as detailed below with reference to Example 1. Optionally,a single dye or stain (e.g. Acridine Orange) may be used with differentlighting conditions, to provide differential staining. Optionally, acombination of dyes or stains is used, comprising one or moreDNA-specific dyes or stains (e.g., Hoechst reagent) and one or moreother dyes or stains (e.g., Acridine Orange) configured to detect anynucleic acid (DNA and RNA).

For some applications, the imaging information is informative of one ormore fields of the bodily sample. As used herein, a “field” is a portionof the bodily sample to be imaged. Typically, this corresponds to anarea on the bottom of a sample carrier holding the sample. When theimages are captured at high magnification, only a fraction of the entireblood sample can be imaged at one time. Therefore, pathogen detectionsystem 10 virtually sub-divides an area to be imaged into a plurality offields, and each field is imaged separately, thereby obtaining aplurality of images informative of the bodily sample, each imageinformative of a respective field. Optionally, the imaged fields do notoverlap, or their degree of overlap is less than 5 percent or less than1 percent of the area. In certain embodiments, each field to be imagedis imaged under one or more different lighting conditions. Optionally,an image of each field is captured a plurality of times at differentlighting conditions. For example, the field may be imaged at least oncein lighting conditions to detect RNA-related fluorescence, at least oncein lighting conditions to detect DNA-related fluorescence, and at leastonce in brightfield.

Reference is now made to FIG. 3, which shows, by way of non-limitingexample, imaging information 300 consisting of a field of a blood samplestained with one or more suitable fluorescent dyes and excited under asuitable lighting condition, in accordance with some applications of thepresent application. As may be observed, due to the dye(s), constituentelements 302 fluoresce, thereby appearing brighter (or, in some cases, adifferent color) than other non-fluorescing constituent elements 304(which in this case include red blood cells) in the sample and allowingfor discerning of stained regions in the sample, some features of whichmay be informative of some specific cell types in the sample.

In certain embodiments, the imaging information is informative of one ormore sample constituent elements, including candidates (i.e.,constituent elements that exhibit characteristics that indicate thatthey may be pathogens, and are therefore candidates for being pathogens)and non-candidates. For some applications, an element is identified as acandidate based upon the element appearing fluoresced when the sample isstained with a suitable fluorescent dye or stain and is excited by asuitable lighting condition, for example, as described in US2012/0169863 to Bachelet, and/or in US 2015/0037806 to Pollak, both ofwhich applications are incorporated herein by reference. Alternativelyor additionally, an element may be identified as a candidate based uponother criteria, such as its size shape, color, proximity to otherelements, etc. As used herein, the term “non-candidate” should beexpansively construed to cover a sample constituent element that is nota candidate.

Referring again to FIG. 2, in step 201, processor 28 extracts from theone or more images, from the imaging information, and/or a portionthereof, one or more sample-informative features of the bodily samplethat are indicative of contextual information related to the bodilysample. Typically, a plurality of sample-informative features areextracted. As used herein, “sample-informative features” includefeatures of the bodily sample which are not directed to a specificcandidate and are usable to provide contextual information that can beused to determine the presence, likelihood of, or characteristics of apathogenic infection in the sample, including, in some embodiments, theclassification of specific candidates. By way of non-limiting examples,sample-informative features can include, for example, features relatedto non-candidate constituents in the sample, or features related to thequantity and/or distribution of cells of a given type in the sample.Features related to non-candidate constituents in the sample caninclude, for example, size-related properties of one or morenon-candidates (including relative size as compared to either anexpected size, or to an observed size of one or more other cells),shape-related properties of one or more non-candidates (includingrelative shape as compared to either an expected shape, or to anobserved shape of one or more other elements), and intensity-relatedproperties of one or more non-candidates (including relative intensityas compared to either an expected intensity, or to an observed intensityof one or more other elements). As used herein, an “expected” value (of,for example, size, shape and/or intensity) is such value as may be knownin advance of analyzing imaging information relating to a given sample.Such values include, for example, population statistic values that areknown or can be calculated (for example, for all humans and/or anysubgroup thereof, based, for example, on age, sex, race, ethnicity,etc.), optionally according to a specific condition (e.g. altitude,treatment of the bodily sample, etc.).

For some applications, sample-informative features include featuresrelated to the distribution of candidates or pathogens within the sampleor portions thereof. For example, if the number of candidates orpathogens found in a given image (or part of an image or a group ofimages covering a continuous portion of the sample) is significantlyhigher than the number of candidates or pathogens found in other partsof the same sample, this may indicate that the high concentration ofcandidates or pathogens found in one part of the sample might be aresult of a local effect that should not affect the diagnosis of thesample. For example, a high concentration of candidates or pathogens(e.g. a high concentration of candidates overlapping red blood cells) inone part of the sample, but not in other parts, can be indicative ofcontamination, e.g., from a drop of blood from another sample thatentered the sample under investigation.

For some applications, some or all of step 201 is performed in apre-processing stage in order to determine, for example, whether some ofthe imaging information is of poor quality as measured by predeterminedcriteria (e.g., brightness, focus, etc.), in which case portions of theimaging information may be excluded from further processing (forexample, as described hereinbelow with reference to Example 6).

In step 202, computer processor 28 identifies one or more constituentelements within the sample as being candidates of a pathogen. Asdescribed hereinabove, an element may be identified as a candidate basedupon the element appearing fluoresced when the sample is stained with asuitable fluorescent dye and excited by a suitable lighting condition,for example, as described in US 2012/0169863 to Bachelet, and/or in US2015/0037806 to Pollak, both of which applications are incorporatedherein by reference. Alternatively or additionally, an element may beidentified as a candidate based upon other criteria, such as shape,size, proximity to other elements (such as red blood cells, or othercandidates), etc.

In step 203, computer processor extracts from the one or more images,from the imaging information, or/or from a portion thereof, one or morecandidate-informative features associated with one or more identifiedcandidates. Typically, for each candidate, a plurality ofcandidate-informative features are extracted. As used herein,“candidate-informative features” include features of the candidate (or,in some cases, constituent elements in close proximity to the candidate,as will be detailed below) useable to provide information fordetermining the likelihood of the given candidate being a pathogen or apart of a pathogen.

By way of non-limiting example, candidate-informative features caninclude features related to: a size of a candidate, a shape of acandidate, a motion of a candidate (based, for example, on a comparisonof at least two at least partially overlapping images captured insequence), and/or an intensity of a candidate.

For some applications, candidate-informative features include a relativelocation of a candidate with respect to other sample constituents (e.g.,a red blood cell). Alternatively or additionally, candidate-informativefeatures include a property of a cell (e.g. red blood cell) that atleast partially overlaps with the candidate (and, optionally, also theamount of overlap), such as a size or shape of cell overlapping thecandidate. For some applications, features related to size and shape ofa cell overlapping the candidate include a relative size and relativeshape of the overlapping cell as compared to an expected size orexpected shape. As used herein, a cell is considered to overlap with acandidate at least partially if, in the imaging information, at least aportion of the cell appears to be co-located with at least a portion ofthe candidate (e.g., at least 20 percent or at least 25 percent of thecandidate).

Optionally, candidate-informative features can include features of otherconstituent elements (e.g., pathogen candidates and/or pathogens) thatare found in close proximity to the candidate. In this context, “closeproximity” can be predefined according to any suitable metric. Forexample, constituents in close proximity to the candidate may includeconstituents located within a distance of up to 2× away from thecandidate, where X is an expected (e.g., average) red blood celldiameter. Accordingly, in some embodiments, candidate-informativefeatures (including features of the candidate, of a cell overlapping thecandidate, and/or features of other constituents) may include or belimited to features that are within close proximity to the candidate.

For some applications, the imaging information or a portion thereof isprocessed for candidate-informative feature extraction at least partlyin a pre-processing stage. In certain embodiments, the pre-processingstage can include extracting sample-informative features to obtaincontextual information, and determining the imaging information which isused to extract candidate-informative features in accordance with theobtained contextual information. For some applications, the portion ofthe imaging information which is used for extractingcandidate-informative features and the portion of the imaginginformation which is used for extracting sample-informative featurespartially or completely overlaps.

It should be noted that steps 201, 202 and 203 can be performed in anyorder. In accordance with some applications, steps 201, 202 and 203 areperformed as a single step and/or are intertwined with one another. Forsome applications, some or all of steps 201, 202 and 203 are performedas a plurality of distinct steps.

Typically, based upon the candidate-informative feature(s) incombination with the sample-informative feature(s), computer processor28 classifies a likelihood of the bodily sample being infected with apathogenic infection. For some applications, the pathogenic infection isdetected by implementing the additional steps indicated in FIG. 2.

For some applications, once at least some candidate-informative featuresare extracted, in step 205, processor 28 classifies the likelihoods ofrespective candidates being pathogens, in accordance with the candidatedata obtained for each respective candidate. As used herein, the term“likelihood of being a pathogen” should be expansively construed tocover either a binary determination (e.g., either a pathogen or anon-pathogen) or a scalar determination (e.g., a number, the value ofwhich reflects the estimated likelihood that the given candidate is apathogen). In certain embodiments, processor 28 classifies thelikelihoods of respective candidates being pathogens using the extractedsample-informative features (e.g., the features extracted in step 201)in combination with the candidate-informative features, as will befurther detailed below, for example, with reference to Examples 1 and 2.This is indicated by the dashed arrow connecting step 201 to step 205,indicating that step 201 is an optional input into step 205.

Typically, subsequent to candidate classifying (i.e., step 205), in step207, processor 28 classifies a likelihood of the bodily sample beinginfected with a pathogenic infection. As used herein, the term“likelihood of the bodily sample being infected” should be expansivelyconstrued to cover either a binary determination (e.g. either infectedor clean) or a scalar determination (e.g. a number, the value of whichreflects the estimated likelihood that the given sample is infected).For some applications, processor 28 classifies the sample based on theclassification of the candidates (extracted in step 205), in combinationwith the sample-informative features (extracted in step 201), as will befurther detailed below, for example, with reference to Examples 1 and 3.

For some applications, in step 209, processor 28 classifies thepathogenic infection as containing one or more given types of pathogen,in accordance with one or more extracted sample-informative featuresand/or candidate-informative features. Classifying the pathogenicinfection as containing one or more given types of pathogen may beperformed using information and/or features that were obtained in one ormore of steps 201, 203, 205, and 207, and/or by performing one or moreadditional steps of feature extraction and classification. For someapplications, in order to classify the pathogenic infection, (a)candidates are classified as given types of pathogens, and (b) theoverall pathogenic infection is classified based upon theclassifications of the individual candidates. For some applications,sample-informative features are used for classifying the individualcandidates as given types of pathogens, and/or for classifying theoverall infection as containing given types of pathogens.

For some applications, classifying the pathogenic infection ascontaining one or more given types of pathogen includes, for example,classifying the pathogenic infection in order to determine species orstrains of pathogens contained within the sample, for example, asfurther detailed below with reference to Examples 4 and 5. Suchdetermination may include or be limited to classifying the pathogen to asingle species or strain, or to a group of several possible species orstrains (at least one of which is contained within the sample) and/orruling out a given species or strain (as a species that is not containedwithin the sample). For some applications, processor 28 classifies thepathogenic infection as containing one or more of Plasmodium, a givenstrain of Plasmodium, Plasmodium of a given age, and/or Plasmodium of agiven age range.

For some applications, the computer processor generates an output to theuser (e.g., on the output device) indicating whether or not the sampleis infected with a pathogen, and indicating a classification of theinfection. For some applications, the computer processor generates anoutput indicating that the presence of an infection within the bodilysample could not be determined with a sufficient degree of reliability,indicating that a portion of the sample should be re-imaged, and/orindicating that a portion of the sample should be re-imaged usingdifferent settings (e.g., using different lighting, using a differentstain, using a different or new sample preparation method, and/or usingdifferent microscope settings). For some applications, in response todetermining that the presence of an infection within the bodily samplecould not be determined with a sufficient degree of reliability, thecomputer processor generates an output indicating that the user shouldtake appropriate user actions (e.g., prepare a new sample, and/or testthe sample using an independent method, etc.). Alternatively oradditionally, the computer processor automatically drives the microscopesystem to re-image a portion of the sample, drives the microscope systemto re-image a portion of the sample using different settings (e.g.,different focus, or different field size), and/or modulates a frame rateat which microscope images are acquired by the microscope system.

It is noted that, for some applications, sample-informative features arenot necessarily derived directly from the images. For example,sample-informative features may include statistical or other informationregarding the candidates and/or other entities within the sample, and/orgeneral characteristics of the sample. In general, the scope of thepresent application includes analyzing a sample on two levels, first ona candidate-by-candidate level, and then on a more general level that isindicative of characteristics of the sample as a whole.

For some applications, based upon candidate-level features, two or moresample-informative features related to the bodily sample are extracted,and a characteristic of the bodily sample is determined, by processingthe two or more sample-informative features. Typically, at least some ofthe candidates are pathogen candidates, and candidate-informativefeatures relating to the pathogen candidates are extracted. For someapplications, candidates of entities such as reticulocytes and/orplatelets are additionally identified, and candidate-informativefeatures relating to these candidates are extracted. For someapplications, the sample-informative features include a number ofpathogen candidates in the sample, type of pathogen candidates in thesample, brightness of the candidates relative to background brightness,a probability of candidates being pathogens, number of candidates thathave a probability of being a pathogen that exceeds a threshold, numberof candidates that have a probability of being a given type of pathogenthat exceeds a threshold, a number of platelets in the sample,brightness of platelets, a number of reticulocytes in the sample, anumber of reticulocytes infected by pathogens in the sample, a proximityof the candidates to red blood cells, and/or a number of red blood cellsin the sample.

In embodiments of the presently disclosed subject matter, fewer, moreand/or different stages than those shown in FIG. 2 may be executed. Inembodiments of the presently disclosed subject matter, one or morestages illustrated in FIG. 2 may be executed in a different order and/orone or more groups of stages may be executed simultaneously.

A number of examples detailing specific non-limiting applications of themethod detailed above will now be provided in order to better understandthe disclosed subject matter.

Example 1: Using Concentration of Reticulocytes as a Sample-InformativeFeature for Classifying a Candidate, and/or for Classifying a PathogenicInfection

As detailed above, in certain embodiments, the sample is stained fordiscerning respective locations of DNA and RNA in the sample. Suchstaining may include, for example, using at least one DNA-specific dyeand at least one RNA-specific dye, or at least one target-specific dye(either DNA or RNA) and at least one dye that stains both DNA and RNA.For some applications, in order to classify the likelihood of acandidate being a pathogen (step 205), the respective locations of RNAand DNA staining in sample are used by the processor to determine if thestaining pattern(s) correspond(s) with the pattern(s) expected for apathogen.

FIG. 4 schematically illustrates candidates 400 a and 400 b, eachcandidate shows an area stained for RNA (RNA portion 402) and an areastained for DNA (DNA portion 404). RNA portion 402 and DNA portion 404may be differentially stained, e.g. using different dyes and/ordifferent lighting, in order to discern the particular boundaries ofeach stained area. As is shown in FIG. 4, in candidate 400 a the DNAportion 404 completely overlaps the RNA portion 402, while in candidate400 b the DNA portion 404 partially overlaps the RNA portion 402.

A candidate which appears to have at least partially overlapping DNA andRNA might be a pathogen. However, the appearance of overlapping RNA andDNA stained regions can also be caused by a different entity orentities, including, for example, a different cell type, or two separatebodies (one of which contains DNA and the other of which contains RNA)seemingly positioned on top of one another.

Mature red blood cells have no detectable DNA or RNA and therefore donot fluoresce when stained for nucleic acids. By contrast, Plasmodiumtrophozoites (which are a type of pathogen) may be detected asDNA-containing and RNA-containing bodies within red blood cells.Therefore, for some applications, in order to identify red blood cellsthat contain pathogens, a staining substance that stains both DNA andRNA (such as, Acridine Orange) is used. Alternatively or additionally, astain that stains only DNA (such as a Hoechst stain) is used.

Howell Jolly bodies are DNA-containing bodies that may be found in redblood cells in some unhealthy conditions. In some cases, the presence ofHowell Jolly bodies in a sample may increase the chance of falsepositive determination of a pathogen infection. Even if a DNA-specificstain is used in conjunction with a stain that stains both DNA and RNA,the Howell Jolly bodies may cause a false positive determination of apathogen infection. Therefore, in some embodiments, differentiationbetween red blood cells that contain Howell Jolly bodies and red bloodcells that contain pathogens may be beneficial.

Young red blood cells, termed reticulocytes, are sometimes found inblood. These cells contain RNA bodies only. A positive correlation isknown between the presence of Howell Jolly bodies in red blood cells anda larger than normal amount of reticulocytes. Therefore, for someapplications, sample-informative features include features that areindicative of a concentration of reticulocytes in a blood sample. (It isnoted that a Plasmodium infection also raises the reticulocyte count fora patient. However, this increase (of, for example, about 5%) istypically much lower than the increase typical of patients that have ahigh Howell Jolly body count (which may be about ten times as great).Accordingly, a threshold for the determination of a high reticulocytecount is typically slightly higher than the average value for humans (ora given sub-population thereof).)

Based upon identifying a high concentration of reticulocytes, thelikelihood of pathogen candidates being Howell Jolly bodies increases.In turn, the likelihood of the candidates being pathogens decreases, andthe likelihood of the sample being infected decreases. Therefore, forsome applications, the computer processor adjusts a threshold for apositive determination of an infection, based upon the concentration ofreticulocytes. For example, many reticulocytes detected concomitantlywith low parasitemia (e.g. less than 200 parasites/microliter blood) maybe interpreted as being indicative of a high probability of a falsepositive (i.e., the sample being non-infected).

Alternatively or additionally, based upon the concentration ofreticulocytes, in order to classify the likelihood of a candidate beinga pathogen (step 205 of FIG. 2), the processor ascribes more weight tothe relative positions of DNA and/or RNA within candidate given redblood cell, rather than simply the presence of DNA and/or RNA within thered blood cell. Alternatively or additionally, based upon theconcentration of reticulocytes, in order to classify the likelihood of asample being infected (step 207 of FIG. 2), the processor ascribes moreweight to extracellular Plasmodium candidates, rather than intracellularPlasmodium candidates (which could be Howell Jolly bodies).

Example 2: Using Distribution of Candidates within a Sample as aSample-Informative Feature for Classifying a Candidate

Candidates within a sample are expected to be uniformly distributed.Therefore, for some applications, a distribution of candidates withinthe sample that differs significantly from an expected uniformdistribution is used as a sample-informative feature. For example, ifthere are significant candidate clusters, the clusters may be foreignbodies associated with the sample carrier rather than a portion of theblood sample, or may indicate that a different infected samplecontaminated the sample being analyzed (for example, by spilling overfrom an adjacent chamber on a sample carrier). In response to detectinga non-uniform distribution of candidate, candidates that are withinlocalized clusters may be given a lower score (i.e., they may beclassified as being less likely to be pathogens). For example, if thesample-informative features are indicative of clustering of candidates,the processor may use distance from the cluster center(s) of any givencandidate as a feature for classifying the candidate.

Example 3: Platelet Concentration as a Sample-Informative Feature forClassifying a Sample as Infected

Platelets typically appear as small extracellular RNA bodies, althoughsome platelets may appear to be overlapped with cells because they arepositioned on or under a cell when the sample is imaged. A normalconcentration of platelets is typically between 150,000-400,000platelets per microliter of whole blood.

It is known that the concentration of platelets may be affected byPlasmodium infection, its severity and the species of Plasmodium, aswell as by other unrelated conditions (including medical conditions,treatments and medications). Accordingly, for some applications, thenumber and/or concentration of platelets in a sample is used as asample-informative feature, and, for example, may be used as an input inclassifying the likelihood of the sample being infected.

Example 4: Platelet Concentration as a Sample-Informative FeatureInformative of a Species of Pathogen

As mentioned in the context of Example 3, the number and/orconcentration of platelets can be correlated with a specific species ofpathogen, for example a low platelet count has been shown to becorrelated with a Plasmodium falciparum infection to a significantlygreater extent than Plasmodium vivax infection. For some applications,in accordance with step 209 of FIG. 2, the number and/or concentrationof platelets in a blood sample is used as an input for classifying apathogenic infection as containing a given type of pathogen.

Example 5: Red Blood Cell Size and Shape as a Sample-Informative Feature

Some pathogens change the morphology of infected cells. For example,some pathogens (e.g., relatively mature trophozoites of Plasmodium vivaxand Plasmodium ovale) cause an enlargement of infected red blood cells,sometimes to about two-fold that of uninfected cells. Other pathogens(e.g., Plasmodium malariae) reduce the size of infected red blood cells.Still other pathogens (e.g., Plasmodium falciparum) do not enlargeinfected cells or reduce their size. For some applications, the sizes ofred blood cells that appear to be infected within a blood sample areused as a sample-informative feature that is indicative of the samplebeing infected (e.g., in step 207 of FIG. 2), and/or is indicative of anidentity of the pathogen (e.g., in step 209 of FIG. 2).

For example, a blood sample infected by Plasmodium vivax and/orPlasmodium ovale is expected to include infected red blood cells thatare significantly enlarged. A blood sample infected by Plasmodiummalariae, on the other hand, is expected to include infected red bloodcells that are significantly diminished in size. Therefore, for someapplications, detection of such enlarged and/or diminished cells is usedas a sample-informative feature that is indicative of the sample beinginfected (e.g., in step 207 of FIG. 2) and/or is indicative of anidentity of the pathogen (e.g., in step 209 of FIG. 2).

In another example, Plasmodium ovale may cause infected red blood cellsto become more oval than uninfected red blood cells that tend to appearround. Accordingly, one or more of the following sample-informativefeatures may be interpreted as being indicative of the sample beinginfected and/or being infected with Plasmodium ovale: the presence ofoval red blood cells in the sample, the presence of a higher thanexpected portion of oval red blood cells, and/or the presence and/oramount of infected red blood cells that appear to be oval.

Features of oval(s) (e.g., height versus width) may be used in aclassification in a weighted manner. For example, the weight given to anoval feature that is close to an expected value may be higher than ifthe value is closer to an expected value for uninfected red blood cellor values that are significantly more deviant than the expected valuefor infected red blood cells (e.g., if the oval appears to be rod like).

It should be noted that a determination that an infected red blood cell(or a group of infected red blood cells), or potentially infected redblood cell, is different in any given property (e.g. size, shape, etc.)than the general population of red blood cells (or different thanuninfected cells), and/or a determination of a degree of suchdifference, is typically reached using any acceptable statistic. Forexample, an average size of two groups may be used and/or an averagesize of one group may be used in relation to a given percentile of theother group. Optionally, a plurality of statistics is used. Optionally,one or more of the values for red blood cells (or for uninfected redblood cells) are taken from known statistics of the general populationor a subgroup thereof. In some embodiments, one or more statistics ofall red blood cells in the sample (or a portion thereof) may be used,rather than using only the uninfected red blood cells. For example, thismay be used in cases in which the portion of infected red blood cellswithin the sample is sufficiently small.

Optionally, a determination that an infected or potentially infected redblood cell is different or relatively different in any given property(e.g. size, shape, etc.) is made by comparing the given property of theinfected red blood cell or potentially infected red blood cell toproperties of one or more clean red blood cells in the sample. As such,properties of one or more clean red blood cells can also be used assample-informative features for determining the likelihood that acandidate is a pathogen, for determining the likelihood that a sample isinfected, and/or for classifying the species of a pathogen.

It should be noted that red blood cell features (e.g., features relatedto red blood cell size and/or shape) can also be used in candidateclassification (e.g., as a candidate-informative feature used in step205 of FIG. 2) when compared with an expected value. By way ofnon-limiting example, candidates which appear to be inside (orco-located with) red blood cells that are relatively large or small thanan expected value or have a shape that is different than an expectedshape (e.g. oval instead of round) are more likely to be pathogens.

It should also be noted that features of red blood cells in the sample(e.g. features related to red blood cell size and/or shape) can be usedin sample classification (e.g., as a sample-informative feature used instep 207 of FIG. 2). By way of non-limiting example, such featuresinclude a statistic taken for a group of red blood cells in the sample(e.g., a statistic for seemingly infected red blood cells in the sample,a statistic for uninfected red blood cells in the sample, and/or astatistic for red blood cells in the sample in general). Onenon-limiting example includes comparing a statistic of seeminglyinfected red blood cells in the sample (e.g., size) to an expected value(e.g., average size of human red blood cell) or to a correspondingstatistic for red blood cells in the sample in general. When theseemingly infected red blood cells are found to be larger or smallerthan the compared value, this may be used as an indication that thesample is infected.

Echinocytes are red blood cells that have abnormal cell membranes withmany small, symmetrically spaced thorny projections. Acanthocytes alsohave thorny abnormal projections, but they are irregular andasymmetrical.

In some cases, a Plasmodium infection causes the appearance ofechinocytes or acanthocytes. Other causes for such shapes may be otherpathologies and even a prolonged exposure to some solutions (e.g., dyesolutions). Additionally, some strains of Plasmodium cause greaterdeformity than other strains. For example, Plasmodium vivax is typicallymore deforming than Plasmodium falciparum, while each of Plasmodiumvivax and Plasmodium ovale is typically more deforming than each ofPlasmodium malariae and Plasmodium falciparum. Therefore, for someapplications, the presence of such shapes in a sample is used as asample-informative feature that is indicative of the sample beinginfected (e.g., in step 207 of FIG. 2), and/or is indicative of anidentity of the pathogen (e.g., in step 209 of FIG. 2).

In some strains of Plasmodium, there is a positive correlation between adegree of red blood cell deformity and the age of the infectingpathogens. Therefore, for some applications, the presence of such shapesin a sample is used as a sample-informative feature that is indicativeof the age of the detected pathogens (e.g., in step 209 of FIG. 2).

Example 6: Using Staining Quality as a Sample-Informative Feature

Staining of a biological sample is known to be a time dependent process.Once cells are exposed to a dye it takes time for the dye to penetratethe cells and reach its target site(s) within the cells. During thisperiod, staining may become sharper and/or more localized with sharperintensity gradients (especially if the sample is not washed before beingimaged). Typically, this process follows a saturation curve. In a firstphase, staining increases relatively fast until some degree of stainingis reached (i.e., a fast staining phase). Thereafter, quality stillincreases but relatively slowly for another period of time (i.e., a slowstaining phase). At a later time, staining quality may deteriorate, forexample, due to photo bleaching (in a fluorescent dye), and/or due tothe dye slowly diffusing through the sample, away from the target(s).

For some applications, imaging a plurality of fields (e.g., 200 fieldsor more, at least some of which may be imaged more than once) of abodily sample, such as a blood sample, may take a few minutes (e.g., 2-3minutes or more). For Hoechst staining (which is used, for example, inthe detection of Plasmodium infection), the fast staining phase may take30 minutes or more.

Accordingly, when imaging a plurality of fields is carried out over theabove-described time scale, there might be a significant variation instaining quality between fields. This variation may affect thediagnostic result (e.g., by changing an intensity value, an intensitygradient value, and/or a threshold for infection). For someapplications, in order to account for the variation in staining quality,staining quality of the sample is used as a sample-informative feature,in accordance with techniques described herein.

For some applications, an average staining quality across a pluralityimages is determined, and a staining quality parameter for each image isdetermined, based on the image's relative staining quality compared tothe average value. The staining quality parameter can be used as asample-informative feature, for example, for discarding, from being usedin further analysis, images that were taken too early or too late, whenstaining is not sufficiently informative for diagnostics. Alternativelyor additionally, the staining quality parameter can be used to adjustone or more thresholds for different fields or groups of fields based onthe staining quality parameter, and/or introduce the staining qualityparameter into the classifying of candidates, so that candidates from aplurality of fields having different staining qualities can be compared.For example, the candidate classification can be normalized using thestaining quality.

For some applications, in response to the staining quality parameter, aframe rate at which images of the bodily sample are acquired ismodulated. For example, in response to detecting that the staining is ofa low quality, images may be acquired at a greater frame rate, and viceversa. Alternatively or additionally, in response to the stainingquality parameter, the number of times each field is imaged ismodulated. In general, the scope of the present invention includes usingsample-informative features as an input for discarding some images frombeing used, for modulating a frame rate at which images are acquired,and/or for modulating the number of times that each imaging field isimaged.

Optionally, when using two or more stains, the staining qualityvariation may differ between the stains. For example, Acridine Orangestaining may be complete, when Hoechst staining is still in the faststaining phase. Accordingly, for some applications, the staining qualityparameter is treated separately for each stain, and/or the relativestaining quality between the stains may be used as a staining qualityparameter.

Example 7: Using Affinity of Candidates for Reticulocytes as aSample-Informative Feature

Plasmodium vivax and Plasmodium ovale have an affinity for infectingreticulocytes, over mature red blood cells. Conversely, Plasmodiumfalciparum infects all red blood cells equally, while Plasmodiummalariae has an affinity for mature red blood cells. Accordingly, forsome applications, a relationship between the number of pathogencandidates associated with reticulocytes and the number of candidatesassociated with mature red blood cells is determined, and this is usedas a sample-informative feature.

As described hereinabove, for some applications, in accordance with step209 of FIG. 2, the processor classifies a pathogen as containing a giventype of pathogen. For some applications, once it is determined that asample is infected, the processor classifies the pathogen as containinga given type of pathogen, based upon the estimated ages of infected redblood cells, and/or based upon a relationship between the number ofpathogen candidates associated with reticulocytes and the number ofcandidates associated with mature red blood cells. For example, if thepathogens have an affinity for reticulocytes, this may be used as anindication that that the detected pathogen is Plasmodium vivax.Alternatively, an essentially uniform distribution of the pathogen inred blood cells of all ages (proportional to relative abundance of thedifferently-aged red blood cells) may be used as an indication that thatthe detected pathogen is Plasmodium falciparum.

Example 8: Using Detection of Contamination as a Sample-InformativeFeature

An image of a blood sample may contain foreign objects that areirrelevant for diagnosis and may be due for example to dirt or flaws ina solution or tool used to prepare or hold the blood sample (e.g., adilution solution and/or a sample chamber used for housing the sample).Such objects may include objects that appear similar to a pathogenand/or objects that are dissimilar to a pathogen.

For some applications of the present invention, the computer processoris configured to extract, from the one or more images, at least onesample-informative feature that is indicative of contextual informationrelated to the bodily sample. At least partially based upon theextracted sample-informative feature, the computer processor identifiesthat there is a defect associated with the bodily sample disposed in thesample carrier, and classifies a source of the defect (for example, asbeing the sample carrier, a given portion of the sample carrier, thesample itself, and/or a diluent in which the sample has been diluted).The computer processor generates an output on the output device that isindicative of the source of the defect.

For example, such an output may be generated based upon asample-informative feature that is indicative of the presence of foreignobjects within the sample. The source of the foreign objects is expectedto affect their concentration and distribution in the sample. Forexample, if the source is the blood sample itself, then the amount offoreign objects that is detected is typically proportional to the sizeof the sample. For some applications, in response to detecting foreignobjects having this characteristic, a threshold for diagnosis isadjusted. For example, the threshold for the number of pathogens withina sample that is sufficient to deem the sample to be infected may be afunction of a relationship between the concentration of foreign objectswithin the sample, to that of red blood cells.

For some applications, the processor is configured to determine that thesource of foreign objects is limited to a given chamber, a given set ofchambers, a given field, or a given set of fields. For such cases, thecomputer processor may ascribe lower weight to features that aredetected in the affected chambers or fields, and/or may use data fromother chambers or fields as inputs to analyzing features detected in theaffected chambers and fields.

For some applications, the computer processor is configured to detectthat the source of foreign objects is a diluent in which the sample hasbeen diluted (for example, in response to detecting foreign objects withcharacteristics that are common to a plurality of samples and/orchambers). In such cases, the processor may generate an outputindicating the likely source of the foreign objects. For someapplications, the computer processor is configured to detectcross-contamination between chambers, and to generate an outputindicating that this is the case.

It is noted that although some applications of the present inventionhave been described with respect to detecting a pathogen infectionwithin a bodily sample, the scope of the present invention includesperforming similar techniques with respect to identifying othercomponents or entities within a bodily sample. For example, similartechniques to those described herein may be used for detecting theconcentration of a given entity within a blood sample, by (a) extractingone or more candidate-informative features associated with an elementthat is a candidate of the given entity, (b) extracting one or moresample-informative feature that are indicative of contextual informationrelated to the bodily sample, and (c) processing thecandidate-informative feature in combination with the sample-informativefeature. For some applications, the sample is a sample that includesblood, and the candidates are candidates of entities within the blood,such as platelets, white blood cells, anomalous white blood cells,circulating tumor cells, red blood cells, reticulocytes, Howell Jollybodies, etc. For some such applications, a blood count (e.g., a completeblood count) is performed by identifying such candidates, and performingtechniques as described herein.

For some applications, the sample is a different bodily sample, and thetechniques described herein are used for identifying a pathogeninfection within the sample. For example, the techniques describedherein may be used to identify Mycobacterium tuberculosis within asample of sputum. Alternatively or additionally, the sample is adifferent bodily sample, and the techniques described herein are usedfor identifying abnormal cells within the sample. For example, thetechniques described herein may be used to identify cancerous cells in aPAP smear or in a urine sample.

In general, it is noted that although some applications of the presentinvention have been described with respect to a blood sample, the scopeof the present invention includes applying the apparatus and methodsdescribed herein to a variety of samples. For some applications, thesample is a bodily sample, such as, blood, saliva, semen, sweat, sputum,vaginal fluid, stool, breast milk, bronchoalveolar lavage, gastriclavage, tears and/or nasal discharge. The bodily sample may be from anyliving creature, and is typically from warm blooded animals. For someapplications, the bodily sample is a sample from a mammal, e.g., from ahuman body. For some applications, the sample is taken from any domesticanimal, zoo animals and farm animals, including but not limited to dogs,cats, horses, cows and sheep. Alternatively or additionally, the bodilysample is taken from animals that act as disease vectors including deeror rats.

For some applications, similar techniques to those described hereinaboveare applied to a non-bodily sample. For some applications, the sample isan environmental sample, such as, water (e.g. groundwater) sample,surface swab, soil sample, air sample, or any combination thereof. Insome embodiments, the sample is a food sample, such as, a meat sample,dairy sample, water sample, wash-liquid sample, beverage sample, and anycombination thereof.

Applications of the invention described herein can take the form of acomputer program product accessible from a computer-usable orcomputer-readable medium (e.g., a non-transitory computer-readablemedium) providing program code for use by or in connection with acomputer or any instruction execution system, such as computer processor28. For the purposes of this description, a computer-usable or computerreadable medium can be any apparatus that can comprise, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Typically, the computer-usable or computer readablemedium is a non-transitory computer-usable or computer readable medium.

Examples of a computer-readable medium include a semiconductor or solidstate memory, magnetic tape, a removable computer diskette, a randomaccess memory (RAM), a read-only memory (ROM), a rigid magnetic disk andan optical disk. Current examples of optical disks include compactdisk-read only memory (CD-ROM), compact disk-read/write (CD-R/W) andDVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor (e.g., computer processor 28)coupled directly or indirectly to memory elements (e.g., memory 29)through a system bus. The memory elements can include local memoryemployed during actual execution of the program code, bulk storage, andcache memories which provide temporary storage of at least some programcode in order to reduce the number of times code must be retrieved frombulk storage during execution. The system can read the inventiveinstructions on the program storage devices and follow theseinstructions to execute the methodology of the embodiments of theinvention.

Network adapters may be coupled to the processor to enable the processorto become coupled to other processors or remote printers or storagedevices through intervening private or public networks. Modems, cablemodem and Ethernet cards are just a few of the currently available typesof network adapters.

Computer program code for carrying out operations of the presentinvention may be written in any combination of one or more programminglanguages, including an object oriented programming language such asJava, Smalltalk, C++ or the like and conventional procedural programminglanguages, such as the C programming language or similar programminglanguages.

It will be understood that blocks of the flowchart shown in FIG. 2 andcombinations of blocks in the flowchart, can be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer (e.g., computer processor 28) or other programmable dataprocessing apparatus, create means for implementing the functions/actsspecified in the flowcharts and/or algorithms described in the presentapplication. These computer program instructions may also be stored in acomputer-readable medium (e.g., a non-transitory computer-readablemedium) that can direct a computer or other programmable data processingapparatus to function in a particular manner, such that the instructionsstored in the computer-readable medium produce an article of manufactureincluding instruction means which implement the function/act specifiedin the flowchart blocks and algorithms. The computer programinstructions may also be loaded onto a computer or other programmabledata processing apparatus to cause a series of operational steps to beperformed on the computer or other programmable apparatus to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowcharts and/oralgorithms described in the present application.

Computer processor 28 is typically a hardware device programmed withcomputer program instructions to produce a special purpose computer. Forexample, when programmed to perform the algorithms described withreference to FIG. 2, computer processor 28 typically acts as a specialpurpose sample-analysis computer processor. Typically, the operationsdescribed herein that are performed by computer processor 28 transformthe physical state of memory 30, which is a real physical article, tohave a different magnetic polarity, electrical charge, or the likedepending on the technology of the memory that is used.

Unless specifically stated otherwise, as apparent from the discussionsherein, throughout the specification discussions utilizing terms such as“processing,”, “executing,” “obtaining,” “determining,” “classifying,”“storing,” “selecting,” or the like, refer to the action(s) and/orprocess(es) of a computer that manipulate and/or transform data intoother data, said data represented as physical, such as electronic,quantities and/or said data representing the physical objects. The terms“computer” and “processor” should be expansively construed to cover anykind of electronic device with data processing capabilities including,by way of non-limiting example, the system disclosed in the presentapplication.

It is to be understood that the term “non-transitory” is used herein toexclude transitory, propagating signals, but to include, otherwise, anyvolatile or non-volatile computer memory technology suitable to thepresently disclosed subject matter.

Typically, computer processor generates an output on output device 34.The output may be provided in any acceptable form, including a graph,graphic or text displayed on a monitor of a control unit, a printout, asa voice message, or on a user's smartphone display, for acceptingprocessed data from the processing utility and displaying informationrelating to the structural features obtained and/or associated valuesdetermining the presence and optionally the identity of a pathogenicinfection, using lists, tables, graphs etc. The output device mayinclude a monitor that is connected to a printer for printing theoutput.

User interface 32 may be used to control the operation of system 10and/or computer processor 28, including, inter alia, inputting data withrespect to the examined bodily sample (e.g., source, date, place, etc.),controlling conditions of operating the system, types of dyes used,number of images to be taken, time interval between images, etc.

At times, image analysis by the computer processor may involveadjustment or normalization of image brightness on the basis of degreeof staining of the sample. These may be based on, for example,identifying one or more of brightest and/or dimmest pixel values in theimage or set of image (for example, corresponding to a particularsample), average brightness of brightest and/or dimmest area, and/orimage histogram. Such features may be extracted from a representativeimage (not necessarily the one being normalized) or from statisticalanalysis of multiple images. The features used for normalization may bebased on a single or multiple images, which may be captured usingdifferent excitation wavelengths (e.g., Acridine Orange providingdifferent colors under different illumination wavelengths). Imagebrightness may also be adjusted using other control means, such as imagecapturing component exposure time and/or brightness of illumination.

The conditions of microscope system 11 may be such as to control thetiming of the image acquisition, e.g., to allow sufficient incubationtime with the one or more dyes or stains as well as the operation withdifferent optical configurations of excitation and/or emissionwavelengths, in order to image the stained sample at various colors orfluorescence spectra.

The components of the pathogen detection system, namely, imaging module14, computer processor 28, output device 34, etc. may be directlyconnected to each other (e.g., directly by a wire) or one or more of thecomponents may be remote from one or more other components. For example,the imaging module may send data to computer processor 28 over anintranet or over the internet, to allow processing at a remote location.

Examples of systems which may be used for performing the techniques ofthe present disclosure are described in WO 2012/090198 to Bachelet andin US 2014/0347459 to Greenfield, both of which applications areincorporated herein by reference.

There is therefore provided the following inventive concepts, inaccordance with some applications of the present invention:

Inventive concept 1. A method of detecting a pathogenic infection in abodily sample, the method comprising:

storing in a memory imaging information related to the bodily sample, atleast a portion of the imaging information being informative of one ormore pathogen candidates in the sample,

providing, by a processor operatively coupled to the memory, a firstprocessing of a first part of the imaging information, the firstprocessing including: extracting at least one sample-informativefeature, and processing the extracted at least one sample-informativefeature to obtain context data indicative of contextual informationrelated to the sample,

providing, by the processor, a second processing of a second part of theimaging information, the second processing including: identifying atleast one pathogen candidate in the sample; extracting at least onecandidate-informative feature associated with the identified candidate,and processing the at least one extracted candidate-informative featureto obtain candidate data indicative of at least one classifying propertyof the candidate,

providing, by the processor, a first classifying, the first classifyingincluding classifying the at least one identified candidate as apathogen or a non-pathogen at least in accordance with the obtainedcandidate data,

providing, by the processor, a second classifying, the secondclassifying including classifying the sample as infected or clean atleast in accordance with the results of the first classifying and theobtained context data,

wherein a pathogenic infection in the bodily sample is determined basedon the results of the second classifying.

Inventive concept 2. The method of inventive concept 1 wherein the firstclassifying is performed further in accordance with the obtained contextdata.Inventive concept 3. The method of inventive concept 1 wherein thesecond classifying is performed further in accordance with the obtainedcandidate data for at least one identified candidate.Inventive concept 4. The method of any one of inventive concepts 1-3,further comprising providing, by the processor, classifying at least onepathogen in the sample at least in accordance with the obtained contextdata.Inventive concept 5. The method of inventive concept 4, whereinclassifying the at least one pathogen includes determining the speciesof the at least one pathogen.Inventive concept 6. The method of any one of inventive concepts 1-5,wherein the at least one candidate-informative feature is selected fromthe group consisting of a feature related to: a size of the candidate, ashape of the candidate, a motion of the candidate, an intensity of thecandidate, a location of the candidate within the sample, and a propertyof a cell overlapping the candidate.Inventive concept 7. The method of inventive concept 6, wherein the cellis a red blood cell, and the property includes at least one of: a sizerelated property and a shape related property.Inventive concept 8. The method of any one of inventive concepts 1-7,wherein the at least one sample-informative feature is selected from thegroup consisting of a feature related to: a size, shape, or intensity ofone or more non-candidate constituents in the sample, a quantity ofcells of a given cell type, a distribution of cells of a given celltype, and a distribution of candidates.Inventive concept 9. The method of any one of inventive concepts 1-8,wherein the bodily sample is selected from a blood sample, a dilutedblood sample, a sample comprising predominantly red blood cells and adiluted sample comprising predominantly red blood cells.Inventive concept 10. A method of detecting a pathogen in a bodilysample, the method comprising:

storing in a memory imaging information related to the bodily sample, atleast a portion of the imaging information being informative of one ormore pathogen candidates in the sample,

providing, by a processor operatively coupled to the memory, a firstprocessing of a first part of the imaging information, the firstprocessing including: extracting at least one sample-informativefeature, and processing the extracted at least one sample-informativefeature to obtain context data indicative of contextual informationrelated to the sample,

providing, by the processor, a second processing of a second part of theimaging information, the second processing including: identifying atleast one pathogen candidate in the sample; extracting at least onecandidate-informative feature associated with the identified candidate,and processing the at least one extracted candidate-informative featureto obtain candidate data indicative of at least one classifying propertyof the candidate,

providing, by the processor, a first classifying, the first classifyingincluding classifying the at least one identified candidate as apathogen or a non-pathogen at least in accordance with the obtainedcandidate data and the obtained context data.

Inventive concept 11. The method of inventive concept 10 furthercomprising:

providing, by the processor, a second classifying, the secondclassifying including classifying at least one pathogen in the sample atleast in accordance with the obtained candidate data.

Inventive concept 12. The method of inventive concept 11, wherein thesecond classifying includes determining the species of the at least onepathogen.Inventive concept 13. The method of any one of inventive concepts 10-12,further comprising: providing, by the processor, a pre-processing of theimaging information, the pre-processing including determining theimaging information to be included in at least one of the first part andthe second part, wherein the pre-processing includes extracting at leastone sample-informative feature from the imaging information, andprocessing the extracted at least one sample-informative feature toobtain context data indicative of contextual information related to thesample, and wherein the determining is made in accordance with theobtained context data.Inventive concept 14. The method of any one of inventive concepts 10-13,wherein the at least one candidate-informative feature is selected fromthe group consisting of a feature related to: a size of the candidate, ashape of the candidate, a motion of the candidate, an intensity of thecandidate, a location of the candidate within the sample, and a propertyof a cell overlapping the candidate.Inventive concept 15. The method of inventive concept 14 wherein thecell is a red blood cell, and the property includes at least one of: asize related property and a shape related property.Inventive concept 16. The method of any one of inventive concepts 10-15,wherein the at least one sample-informative feature is selected from thegroup consisting of a feature related to: a size, shape, or intensity ofone or more non-candidate constituents in the sample, a quantity ofcells of a given cell type, a distribution of cells of a given celltype, and a distribution of candidates.Inventive concept 17. The method of any one of inventive concepts 10-16,wherein the bodily sample is selected from a blood sample, a dilutedblood sample, a sample comprising predominantly red blood cells and adiluted sample comprising predominantly red blood cells.Inventive concept 18. A system for detecting a pathogenic infection in abodily sample, comprising:

a memory operatively coupled to a digital microscope and configured tostore imaging information captured by the digital microscope, theimaging information related to a bodily sample, at least a portion ofthe imaging information being informative of one or more pathogencandidates in the sample; and

a processor operatively coupled to the memory and configured to:

-   -   process, in a first processing, a first part of the imaging        information, the first processing including: extracting at least        one sample-informative feature, and processing the extracted at        least one sample-informative feature to obtain context data        indicative of contextual information related to the sample,    -   process, in a second processing, a second part of the imaging        information, the second processing including: identifying at        least one pathogen candidate in the sample; extracting at least        one candidate-informative feature associated with the identified        candidate, and processing the at least one extracted        candidate-informative feature to obtain candidate data        indicative of at least one classifying property of the        candidate,    -   classify, in a first classifying, the at least one identified        candidate as a pathogen or a non-pathogen at least in accordance        with the obtained candidate data,    -   classify, in a second classifying, the sample as infected or        clean at least in accordance with the results of the first        classifying and the obtained context data,

wherein a pathogenic infection in the bodily sample is determined basedon the results of the second classifying.

Inventive concept 19. The system of inventive concept 18, wherein thefirst classifying is performed further in accordance with the obtainedcontext data.Inventive concept 20. The system of any one of inventive concepts 18 or19, wherein the second classifying is performed further in accordancewith the obtained candidate data for at least one identified candidate.Inventive concept 21. The system of any one of inventive concepts 18-20,wherein the processor is further configured to, prior to the first andsecond processing:

pre-process the imaging information, the pre-processing includingdetermining the imaging information to be included in at least one ofthe first part and the second part, wherein the pre-processing includesextracting at least one sample-informative feature from the imaginginformation, and processing the extracted at least onesample-informative feature to obtain context data indicative ofcontextual information related to the sample, and wherein thedetermining is made in accordance with the obtained context data.

Inventive concept 22. The system of any one of inventive concepts 18-21,wherein the processor is further configured to classify a pathogen inthe sample at least in accordance with the obtained context data.Inventive concept 23. The system of any one of inventive concepts 18-22,wherein the at least one candidate-informative feature is selected fromthe group consisting of a feature related to: a size of the candidate, ashape of the candidate, a motion of the candidate, an intensity of thecandidate, a location of the candidate within the sample, and a propertyof a cell overlapping the candidate.Inventive concept 24. The system of inventive concept 23, wherein thecell is a red blood cell, and the property includes at least one of: asize related property and a shape related property.Inventive concept 25. The system of any one of inventive concepts 18-24,wherein the at least one sample-informative feature is selected from thegroup consisting of a feature related to: a size, shape, or intensity ofone or more non-candidate constituents in the sample, a quantity ofcells of a given cell type, a distribution of cells of a given cell typeand a distribution of candidates.Inventive concept 26. The system of any one of inventive concepts 18-25,wherein the bodily sample is selected from a blood sample, a dilutedblood sample, a sample comprising predominantly red blood cells and adiluted sample comprising predominantly red blood cells.Inventive concept 27. A computer program product implemented on anon-transitory computer usable medium having computer readable programcode embodied therein to cause the computer to perform a method ofdetecting a pathogenic infection in a bodily sample, the methodcomprising:

storing in a memory comprised in or operatively coupled to the computer,imaging information related to the bodily sample, at least a portion ofthe imaging information being informative of one or more pathogencandidates in the sample,

providing, by a processor comprised in or operatively coupled to thecomputer, and operatively coupled to the memory, a first processing of afirst part of the imaging information, the first processing including:extracting at least one sample-informative feature, and processing theextracted at least one sample-informative feature to obtain context dataindicative of contextual information related to the sample,

providing, by the processor, a second processing of a second part of theimaging information, the second processing including: identifying atleast one pathogen candidate in the sample; extracting at least onecandidate-informative feature associated with the identified candidate,and processing the at least one extracted candidate-informative featureto obtain candidate data indicative of at least one classifying propertyof the candidate,

providing, by the processor, a first classifying, the first classifyingincluding classifying the at least one identified candidate as apathogen or a non-pathogen at least in accordance with the obtainedcandidate data, and

providing, by the processor, a second classifying, the secondclassifying including classifying the sample as infected or clean atleast in accordance with the results of the first classifying and theobtained context data.

It will be appreciated by persons skilled in the art that the presentinvention is not limited to what has been particularly shown anddescribed hereinabove. Rather, the scope of the present inventionincludes both combinations and subcombinations of the various featuresdescribed hereinabove, as well as variations and modifications thereofthat are not in the prior art, which would occur to persons skilled inthe art upon reading the foregoing description.

1. A method comprising: staining a blood sample with one or more stains;acquiring a plurality of microscopic images of the stained blood sample,using a microscope; using at least one computer processor, determiningstaining-quality parameters for respective microscopic images, thestaining-quality parameters being indicative of a quality of thestaining within each of the respective microscopic images; and using theat least one computer processor, performing an action based upon thestaining-quality parameters of the respective microscopic images.
 2. Themethod according to claim 1, wherein performing the action based uponthe staining-quality parameters of the respective microscopic imagescomprises discarding at least some of the microscopic images from beingused in an analysis of the blood sample in response to determining thatthe staining quality of the at least some of the microscopic images isnot sufficient.
 3. The method according to claim 1, wherein performingthe action based upon the staining-quality parameters of the respectivemicroscopic images comprises adjusting one or more thresholds that areused for identifying entities within respective microscopic images,based upon the staining-quality parameters of the respective microscopicimages.
 4. The method according to claim 1, wherein performing theaction based upon the staining-quality parameters of the respectivemicroscopic images comprises modulating a number of times respectivemicroscopic imaging fields of the sample are imaged by the microscope,based upon the staining-quality parameters of the respective microscopicimages.
 5. The method according to claim 1, wherein staining the bloodsample with one or more stains comprises staining the blood sample withtwo or more stains, and wherein determining the staining-qualityparameters for respective microscopic images comprises, for each of therespective microscopic images determining a respective staining-qualityparameter for each of the two or more stains.
 6. The method according toclaim 5, wherein staining the blood sample with two or more stainscomprises staining the blood sample with Acridine Orange and a Hoechstreagent.
 7. The method according to claim 1, wherein performing theaction based upon the staining-quality parameters of the respectivemicroscopic images comprises classifying candidates that are identifiedwithin respective microscopic images at least partially based upon thestaining-quality parameters of the respective microscopic images.
 8. Themethod according to claim 7, wherein classifying candidates that areidentified within respective microscopic images comprises normalizingcandidate classification within respective microscopic images based uponthe staining-quality parameters of the respective microscopic images. 9.The method according to claim 1, wherein performing the action basedupon the staining-quality parameters of the respective microscopicimages comprises modulating a parameter of image capture of themicroscopic images, based upon the staining-quality parameters of therespective microscopic images.
 10. The method according to claim 9,wherein modulating the parameter of image capture of the microscopicimages comprises modulating a frame rate at which microscopic images ofthe sample are acquired, based upon the staining-quality parameters ofthe respective microscopic images.
 11. The method according to claim 10,wherein modulating a frame rate at which microscopic images of thesample are acquired based upon the staining-quality parameters of therespective microscopic images comprises acquiring microscopic images ata greater frame rate in response to detecting that staining quality ofat least some of the microscopic images is relatively low, and acquiringmicroscopic images at a lower frame rate in response to detecting thatstaining quality of at least some of the microscopic images isrelatively high.
 12. The method according to claim 1, wherein acquiringthe plurality of microscopic images of the stained blood samplecomprises acquiring a plurality of microscopic images of respectiveimaging fields of the stained blood sample over a time period that issuch that there is variation in staining quality in the microscopicimages of the respective imaging fields.
 13. The method according toclaim 12, wherein determining the staining-quality parameters for therespective microscopic images comprises determining an average stainingquality based on staining qualities of a plurality of microscopicimages, and comparing a staining quality of each of the respectivemicroscopic images to the average staining quality.
 14. The methodaccording to claim 12, wherein acquiring the plurality of microscopicimages of respective imaging fields of the stained blood sample over thetime period that is such that there is variation in staining quality inthe microscopic images of the respective imaging fields comprisesacquiring a plurality of fluoroscopic microscopic images of respectiveimaging fields of the stained blood sample over a time period that issuch that there is variation in staining quality of the microscopicimages of the respective imaging fields due to photobleaching.
 15. Themethod according to claim 12, wherein acquiring the plurality ofmicroscopic images of respective imaging fields of the stained bloodsample over the time period that is such that there is variation instaining quality in the microscopic images of the respective imagingfields comprises acquiring a plurality of microscopic images ofrespective imaging fields of the stained blood sample over a time periodthat is such that there is variation in staining quality in themicroscopic images of the respective imaging fields due to at least oneof the one or more stains diffusing through the blood sample away fromtargets.
 16. Apparatus for analyzing a blood sample that is stained withone or more stains, the apparatus comprising: a microscope systemconfigured to acquire a plurality of microscopic images of the stainedblood sample; an output device; and at least one computer processorconfigured to: determine staining-quality parameters for respectivemicroscopic images, the staining-quality parameters being indicative ofa quality of the staining within each of the respective microscopicimages; and perform an action based upon the staining-quality parametersof the respective microscopic images.
 17. The apparatus according toclaim 16, wherein the at least one computer processor is configured toperform the action based upon the staining-quality parameters of therespective microscopic images by discarding at least some of the imagesfrom being used in an analysis of the blood sample in response todetermining that the staining quality of the at least some of the imagesis not sufficient.
 18. The apparatus according to claim 16, wherein theat least one computer processor is configured to perform the actionbased upon the staining-quality parameters of the respective microscopicimages by adjusting one or more thresholds that are used for identifyingentities within respective microscopic images, based upon thestaining-quality parameters of the respective microscopic images. 19.The apparatus according to claim 16, wherein the at least one computerprocessor is configured to perform the action based upon thestaining-quality parameters of the respective microscopic images bymodulating a number of times respective microscopic imaging fields ofthe sample are imaged by the microscope, based upon the staining-qualityparameters of the respective microscopic images.
 20. The apparatusaccording to claim 16, wherein the apparatus is for use with a bloodsample that is stained with two or more stains, and wherein the at leastone computer processor is configured to determine, for each of therespective microscopic images, a respective staining-quality parameterfor each of the two or more stains.
 21. The apparatus according to claim20, wherein the apparatus is for use with a blood sample that is stainedwith Acridine Orange and a Hoechst reagent and wherein the at least onecomputer processor is configured to determine, for each of therespective microscopic images, a respective staining-quality parameterfor each of the Acridine Orange and the Hoechst reagent.
 22. Theapparatus according to claim 16, wherein the at least one computerprocessor is configured to perform the action based upon thestaining-quality parameters of the respective microscopic images byclassifying candidates that are identified within respective microscopicimages at least partially based upon the staining-quality parameters ofthe respective microscopic images.
 23. The apparatus according to claim22, wherein the at least one computer processor is configured toclassify candidates that are identified within respective microscopicimages by normalizing candidate classification within respectivemicroscopic images based upon the staining-quality parameters of therespective microscopic images.
 24. The apparatus according to claim 16,wherein the at least one computer processor is configured to perform theaction based upon the staining-quality parameters of the respectivemicroscopic images by modulating a parameter of image capture of themicroscopic images, based upon the staining-quality parameters of therespective microscopic images.
 25. The apparatus according to claim 24,wherein the at least one computer processor is configured to modulatethe parameter of image capture of the microscopic images by modulating aframe rate at which microscopic images of the sample are acquired, basedupon the staining-quality parameters of the respective microscopicimages.
 26. The apparatus according to claim 25, wherein the at leastone computer processor is configured to modulate the frame rate at whichmicroscopic images of the sample are acquired based upon thestaining-quality parameters of the respective microscopic images byacquiring microscopic images at a greater frame rate in response todetecting that staining quality of at least some of the microscopicimages is relatively low, and acquiring microscopic images at a lowerframe rate in response to detecting that staining quality of at leastsome of the microscopic images is relatively high.
 27. The apparatusaccording to claim 16, wherein the at least one computer processor isconfigured to acquire the plurality of microscopic images of the stainedblood sample by acquiring a plurality of microscopic images ofrespective imaging fields of the stained blood sample over a time periodthat is such that there is variation in staining quality of themicroscopic images of the respective imaging fields.
 28. The apparatusaccording to claim 27, wherein the at least one computer processor isconfigured to determine the staining-quality parameter for respectivemicroscopic images by determining an average staining quality based onstaining qualities of a plurality of microscopic images, and comparing astaining quality of each of the respective microscopic images to theaverage staining quality.
 29. The apparatus according to claim 27,wherein the at least one computer processor is configured to acquire theplurality of microscopic images of respective imaging fields of thestained blood sample over the time period that is such that there isvariation in staining quality in the microscopic images of therespective imaging fields by acquiring a plurality of fluoroscopicmicroscopic images of respective imaging fields of the stained bloodsample over a time period that is such that there is variation instaining quality in the microscopic images of the respective imagingfields due to photobleaching.
 30. The apparatus according to claim 27,wherein the at least one computer processor is configured to acquire theplurality of microscopic images of respective imaging fields of thestained blood sample over the time period that is such that there isvariation in staining quality in the microscopic images of therespective imaging fields by acquiring a plurality of microscopic imagesof respective imaging fields of the stained blood sample over a timeperiod that is such that there is variation in staining quality in themicroscopic images of the respective imaging fields due to at least oneof the one or more stains diffusing through the blood sample away fromtargets.